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Causality in a social world : moderation, meditation and spill-over /
Causality in a Social World introduces innovative new statistical research and strategies for investigating moderated intervention effects, mediated intervention effects, and spill-over effects using experimental or quasi-experimental data. The book uses potential outcomes to define causal effects,...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
West Sussex :
Wiley,
2015.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Title Page; Copyright Page; Contents; Preface; Part I Overview; Chapter 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.1.1 Moderated treatment effects; 1.1.2 Mediated treatment effects; 1.1.3 Spill-over effects of a treatment; 1.2 Weighting methods for causal inference; 1.3 Objectives and organization of the book; 1.4 How is this book situated among other publications on related topics?; References; Chapter 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.1.1 Attributes versus causes.
- 2.1.2 Potential outcomes and individual-specific causal effects2.1.3 Inference about population average causal effects; 2.1.3.1 Prima facie effect; 2.1.3.2 Ignorability assumption; 2.2 Applications to Lordś paradox and Simpsonś paradox; 2.2.1 Lordś paradox; 2.2.2 Simpsonś paradox; 2.3 Identification and estimation; 2.3.1 Selection bias; 2.3.2 Sampling bias; 2.3.3 Estimation efficiency; Appendix 2.1: Potential bias in a prima facie effect; Appendix 2.2: Application of the causal inference theory to Lord's paradox; References.
- Chapter 3 Review of causal inference designs and analytic methods3.1 Experimental designs; 3.1.1 Completely randomized designs; 3.1.2 Randomized block designs; 3.1.3 Covariance adjustment for improving efficiency; 3.1.4 Multilevel experimental designs; 3.2 Quasiexperimental designs; 3.2.1 Nonequivalent comparison group designs; 3.2.2 Other quasiexperimental designs; 3.3 Statistical adjustment methods; 3.3.1 ANCOVA and multiple regression; 3.3.1.1 ANCOVA for removing selection bias; 3.3.1.2 Potential pitfalls of ANCOVA with a vast between-group difference.
- 3.3.1.3 Bias due to model misspecification3.3.2 Matching and stratification; 3.3.3 Other statistical adjustment methods; 3.3.3.1 The IV method; 3.3.3.2 DID analysis; 3.4 Propensity score; 3.4.1 What is a propensity score?; 3.4.2 Balancing property of the propensity score; 3.4.3 Pooling conditional treatment effect estimate: Matching, stratification, and covariance adjustment; 3.4.3.1 Propensity score matching; 3.4.3.2 Propensity score stratification; 3.4.3.3 Covariance adjustment for the propensity score; 3.4.3.4 Sensitivity analysis.
- Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interactionAppendix 3.B: Variable selection for the propensity score model; References; Chapter 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling; 4.1.1 Simple random sample; 4.1.2 Proportionate sample; 4.1.3 Disproportionate sample; 4.2 Weighting adjustment for selection bias in causal inference; 4.2.1 Experimental result; 4.2.2 Quasiexperimental result; 4.2.3 Sample weight for bias removal; 4.2.4 IPTW for bias removal; 4.3 MMWS.